Special Issue "Machine Learning for Metabolomics Volume 2"

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 496

Special Issue Editors

Department of Computer Science, Tufts University, Medford, MA 02155, USA
Interests: metabolomics; systems biology; machine learning
School of Computing Science, University of Glasgow, Glasgow, UK
Interests: machine learning; metabolomics; mass spectrometry data acquisition; mass spectrometry data analysis; computational biology
Special Issues, Collections and Topics in MDPI journals
Bioinformatics Group, Department of Plant Sciences, Wageningen University, 6708 PB Wageningen, The Netherlands
Interests: metabolomics; metabolite annotation; metabolite identification; metabolome mining; mass spectrometry; mass fragmentation; machine learning-based approaches; substructures; chemical classes; natural product discovery; food metabolome
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine Learning (ML) techniques are transforming many applications, from self-driving cars, to language translation, to online recommendation systems such as those from Amazon and Netflix. This Special Issue will explore how ML is transforming metabolomics as a field. The issue will aim to highlight (i) how to effectively use ML to develop new tools and analysis capabilities, (ii) how to create new ML approaches that support the unique aspects of metabolomics data and workflows, and (iii) how ML use is advancing studies that utilize metabolomics datasets. Reviews and forward-looking contributions that highlight ML’s transformative potential are also invited. Contributions covering comparative studies of ML and non-ML approaches and how we, as a community, share benchmark problems and datasets that measure ML progress in metabolomics are welcome. ML techniques include but are not limited to traditional ML techniques, such as support vector machines and random forests, and deep learning techniques, such as variational inference, gaussian processes, generative models, natural language processing, sequence models, graph neural networks, Bayesian models, reinforcement learning, active learning, time-series models, and others. Applications that use ML to analyze metabolomics along with other omics or other measurement modalities (e.g., imaging) will further highlight the transformative potential of ML in advancing biological discovery and our understanding of human health and disease, plant biotechnology, toxicology, pharmacology, and other areas.

I hope you and yours are well during these difficult times, and I look forward to your submissions. Please do not hesitate to reach out with any questions.

Prof. Dr. Soha Hassoun
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • metabolomics
  • machine learning

Published Papers

There is no accepted submissions to this special issue at this moment.
Back to TopTop